Biomedical subjects

Subhash Ajmani

Publications and source records attributed to Subhash Ajmani.

3 recordsLinked to original sources

PubMed2004

Modeling and interactions of Aspergillus fumigatus lanosterol 14-alpha demethylase 'A' with azole antifungals.

Recent identification of the sterol 14-alpha demethylase genes (CYP51 A and B) from Aspergillus fumigatus and other species by Mellado et al. (J. Clin. Microbiol. 2001, 39(7), 2431-2438), has opened up possibilities of investigating the interactions of azole antifungals with the enzyme(s) from fungi. This study describes for the first time, a model of the three-dimensional structure of A. fumigatus 14-alpha demethylase (AF-CYP51A), using the crystal structure of Mycobacterium tuberculosis 14-alpha demethylase (PDB code:1EA1) as a template. The paper also describes the various interactions between azole antifungals and the target from A. fumigatus (AF-CYP51A). Quantitative evaluation of these interactions is done using COMBINE analysis to understand contributions of active site residues to ligand activity. It also provides explanation for the activity/inactivity of different ligands for AF-CYP51A.

Amino Acid Motifs↗

PubMedDate not supplied

Three-dimensional QSAR using the k-nearest neighbor method and its interpretation.

In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA approach was used to generate models for three different data sets and predict the activity of test molecules through each of these models. The three data sets used were the standard steroid benchmark, an antiinflammatory and an anticancerous data set. The study resulted in kNN-MFA models having better statistical parameters than the reported CoMFA models for all the three data sets. It was also found that stochastic methods generate better models resulting in more accurate predictions as compared to stepwise forward selection procedures. Thus, kNN-MFA method represents a good alternative to CoMFA-like methods.

Journal Article↗

PubMedDate not supplied

Application of QSPR to mixtures.

In this paper we report an attempt to apply the QSPR approach for the analysis of data for mixtures. This is an extension of the conventional QSPR approach to the analysis of data for single molecules. The QSPR methodology was applied to a data set of experimental measured density of binary liquid mixtures compiled from the literature. The present study is aimed to develop models to predict the "delta" value of a mixture i.e., deviation of the experimental mixture density (MED) from the ideal, mole-weighted calculated mixture density (MCD). The QSPR was investigated in two perspectives (QMD-I and QMD-II) with respect to the creation of training and test sets. The study resulted in significant ensemble neural network and k-nearest neighbor models having statistical parameters r2, q2(10cv) greater than 0.9, and pred_r2 greater than 0.75. The developed models can be used to predict the delta and hence the density of a new mixture. The QSPR analysis shows the importance of hydrogen bond, polar, shape, and thermodynamic descriptors in determining mixture density, thus aiding in the understanding of molecular interactions important in molecular packing in the mixtures.

Ligands↗